Learning to represent, categorise and rank in community question answering

The task of Question Answering (QA) is arguably one of the oldest tasks in Natural Language Processing, attracting high levels of interest from both industry and academia. However, most research has focused on factoid questions, e.g. Who is the president of Ireland? In contrast, research on answering non-factoid questions, such as manner, reason, difference and opinion questions, has been rather piecemeal. This was largely due to the absence of available labelled data for the task. This is changing, however, with the growing popularity of Community Question Answering (CQA) websites, such as Quora, Yahoo! Answers and the Stack Exchange family of forums. These websites provide natural labelled data allowing us to apply machine learning techniques. Most previous state-of-the-art approaches to the tasks of CQA-based question answering involved handcrafted features in combination with linear models. In this thesis we hypothesise that the use of handcrafted features can be avoided and the tasks can be approached with representation learning techniques, specifically deep learning. In the first part of this thesis we give an overview of deep learning in natural language processing and empirically evaluate our hypothesis on the task of detecting semantically equivalent questions, i.e. predicting if two questions can be answered by the same answer. In the second part of the thesis we address the task of answer ranking, i.e. determining how suitable an answer is for a given question. In order to determine the suitability of representation learning for the task of answer ranking, we provide a rigorous experimental evaluation of various neural architectures, based on feedforward, recurrent and convolutional neural networks, as well as their combinations. This thesis shows that deep learning is a very suitable approach to CQA-based QA, achieving state-of-the-art results on the two tasks we addressed

Cross-Language Question Re-Ranking

We study how to find relevant questions in community forums when the language of the new questions is different from that of the existing questions in the forum. In particular, we explore the Arabic-English language pair. We compare a kernel-based system with a feed-forward neural network in a scenario where a large parallel corpus is available for training a machine translation system, bilingual dictionaries, and cross-language word embeddings. We observe that both approaches degrade the performance of the system when working on the translated text, especially the kernel-based system, which depends heavily on a syntactic kernel. We address this issue using a cross-language tree kernel, which compares the original Arabic tree to the English trees of the related questions. We show that this kernel almost closes the performance gap with respect to the monolingual system. On the neural network side, we use the parallel corpus to train cross-language embeddings, which we then use to represent the Arabic input and the English related questions in the same space. The results also improve to close to those of the monolingual neural network. Overall, the kernel system shows a better performance compared to the neural network in all cases.Comment: SIGIR-2017; Community Question Answering; Cross-language Approaches; Question Retrieval; Kernel-based Methods; Neural Networks; Distributed Representation